Online courses directory (209)

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Starts : 2017-09-07
No votes
edX Free Closed [?] English Biology & Life Sciences Data Analysis & Statistics EdX HarvardX Science

We will explain how to start with raw data, and perform the standard processing and normalization steps to get to the point where one can investigate relevant biological questions. Throughout the case studies, we will make use of exploratory plots to get a general overview of the shape of the data and the result of the experiment. We start with RNA-seq data analysis covering basic concepts of RNA-seq and a first look at FASTQ files. We will also go over quality control of FASTQ files; aligning RNA-seq reads; visualizing alignments and move on to analyzing RNA-seq at the gene-level: counting reads in genes; Exploratory Data Analysis and variance stabilization for counts; count-based differential expression; normalization and batch effects. Finally, we cover RNA-seq at the transcript-level: inferring expression of transcripts (i.e. alternative isoforms); differential exon usage. We will learn the basic steps in analyzing DNA methylation data, including reading the raw data, normalization, and finding regions of differential methylation across multiple samples. The course will end with a brief description of the basic steps for analyzing ChIP-seq datasets, from read alignment, to peak calling, and assessing differential binding patterns across multiple samples.

Given the diversity in educational background of our students we have divided the series into seven parts. You can take the entire series or individual courses that interest you. If you are a statistician you should consider skipping the first two or three courses, similarly, if you are biologists you should consider skipping some of the introductory biology lectures. Note that the statistics and programming aspects of the class ramp up in difficulty relatively quickly across the first three courses. By the third course will be teaching advanced statistical concepts such as hierarchical models and by the fourth advanced software engineering skills, such as parallel computing and reproducible research concepts.

These courses make up 2 XSeries and are self-paced:

statistics-r-life-sciences-harvardx-ph525-1x-0" target="_blank">PH525.1x: Statistics and R for the Life Sciences

PH525.2x: Introduction to Linear Models and Matrix Algebra

statistics-life-sciences-harvardx-ph525-3x" target="_blank">PH525.3x: Statistical Inference and Modeling for High-throughput Experiments

PH525.4x: High-Dimensional Data Analysis

PH525.5x: Introduction to Bioconductor: annotation and analysis of genomes and genomic assays 

PH525.6x: High-performance computing for reproducible genomics

PH525.7x: Case studies in functional genomics


This class was supported in part by NIH grant R25GM114818.

HarvardX requires individuals who enroll in its courses on edX to abide by the terms of the edX honor code. HarvardX will take appropriate corrective action in response to violations of the edX honor code, which may include dismissal from the HarvardX course; revocation of any certificates received for the HarvardX course; or other remedies as circumstances warrant. No refunds will be issued in the case of corrective action for such violations. Enrollees who are taking HarvardX courses as part of another program will also be governed by the academic policies of those programs.

HarvardX pursues the science of learning. By registering as an online learner in an HX course, you will also participate in research about learning. Read our research statement to learn more.

Harvard University and HarvardX are committed to maintaining a safe and healthy educational and work environment in which no member of the community is excluded from participation in, denied the benefits of, or subjected to discrimination or harassment in our program. All members of the HarvardX community are expected to abide by Harvard policies on nondiscrimination, including sexual harassment, and the edX Terms of Service. If you have any questions or concerns, please contact harvardx@harvard.edu and/or report your experience through the edX contact form.

Starts : 2015-06-22
No votes
edX Free Closed [?] English Biology & Life Sciences Data Analysis & Statistics EdX HarvardX Science

In the PH525 case studies, we will explore the data analysis of an experimental protocol in depth, using various open source software, including R and Bioconductor. We will explain how to start with raw data, and perform the standard processing and normalization steps to get to the point where one can investigate relevant biological questions. Throughout the case studies, we will make use of exploratory plots to get a general overview of the shape of the data and the result of the experiment.

We will learn the basic steps in analyzing DNA methylation data, including reading the raw data, normalization, and finding regions of differential methylation across multiple samples.

This class was supported in part by NIH grant R25GM114818.

This course is part of a larger set of 8 total courses running Self-Paced through September 15th, 2015:

statistics-with-r-for-life-sciences-harvardx-ph525-1x">PH525.1x: Statistics and R for the Life Sciences

PH525.2x: Introduction to Linear Models and Matrix Algebra

statistics-for-the-life-sciences-harvardx-ph525-3x">PH525.3x: Advanced Statistics for the Life Sciences

PH525.4x: Introduction to Bioconductor

PH525.5x: Case study: RNA-seq data analysis

PH525.6x: Case study: Variant Discovery and Genotyping

PH525.7x: Case study: ChIP-seq data analysis

PH525.8x: Case study: DNA methylation data analysis


HarvardX requires individuals who enroll in its courses on edX to abide by the terms of the edX honor code. HarvardX will take appropriate corrective action in response to violations of the edX honor code, which may include dismissal from the HarvardX course; revocation of any certificates received for the HarvardX course; or other remedies as circumstances warrant. No refunds will be issued in the case of corrective action for such violations. Enrollees who are taking HarvardX courses as part of another program will also be governed by the academic policies of those programs.

HarvardX pursues the science of learning. By registering as an online learner in an HX course, you will also participate in research about learning. Read our research statement to learn more.

Harvard University and HarvardX are committed to maintaining a safe and healthy educational and work environment in which no member of the community is excluded from participation in, denied the benefits of, or subjected to discrimination or harassment in our program. All members of the HarvardX community are expected to abide by Harvard policies on nondiscrimination, including sexual harassment, and the edX Terms of Service. If you have any questions or concerns, please contact harvardx@harvard.edu and/or report your experience through the edX contact form.

Starts : 2014-05-12
5 votes
Coursera Free Business English Health & Society Statistics and Data Analysis

Learn to frame and address health-related questions using modern biostatistics ideas and methods.

No votes
OLI. Carnegie Mellon University Free Mathematics Carnegie Mellon University Open Learning Initiative

This course provides an introduction to causal and statistical reasoning. After taking this course, students will be better prepared to make rational decisions about their own lives and about matters of social policy. They will be able to assess criticallyeven if informallyclaims that they encounter during discussions or when considering a news article or report. A variety of materials are presented, including Case Studies where students are given the opportunity to examine a causal claim, and the Causality Lab, a virtual environment to simulate the science of causal discovery. Students have frequent opportunities to check their understanding and practice their skills. This course is meant to serve students in several situations. One, it is meant for students who will only take one such research methods course, and are interested in gaining basic skills that will help them to think critically about claims they come across in their daily lives, such as through a news article. Two, it is meant for students who will take a few statistics courses in service of a related field of study. Three, it is meant for students interested in the foundations of quantitative causal models: called Bayes Networks.

No votes
Study.com Free Closed [?] Mathematics Common Core EPA

Build your earth science vocabulary and learn about cycles of matter and types of sedimentary rocks through the Education Portal course Earth Science 101: Earth Science. Our series of video lessons and accompanying self-assessment quizzes can help you boost your scientific knowledge ahead of the Excelsior Earth Science exam . This course was designed by experienced educators and examines both science basics, like experimental design and systems of measurement, and more advanced topics, such as analysis of rock deformation and theories of continental drift.

Starts : 2006-09-01
14 votes
MIT OpenCourseWare (OCW) Free Engineering Biological Engineering MIT OpenCourseWare Undergraduate

This course covers the analytical, graphical, and numerical methods supporting the analysis and design of integrated biological systems. Topics include modularity and abstraction in biological systems, mathematical encoding of detailed physical problems, numerical methods for solving the dynamics of continuous and discrete chemical systems, statistics and probability in dynamic systems, applied local and global optimization, simple feedback and control analysis, statistics and probability in pattern recognition.

An official course Web site and Wiki is maintained on OpenWetWare: 20.181 Computation for Biological Engineers.

13 votes
Udemy Free Closed [?] Mathematics Math and Science

Lectures by Prof. S.K.RayrnDepartment of Mathematics and StatisticsrnIIT Kanpur

13 votes
Udemy Free Closed [?] Math and Science

Reduce uncertainty with robust designs, dynamic systems theory, nonlinear dynamics, control theory, and statistics

5 votes
Saylor.org Free Closed [?] Mathematics Computer Science Math and Science Statistics Statistics and Data Analysis

In this course, you will look at the properties behind the basic concepts of probability and statistics and focus on applications of statistical knowledge.  You will learn about how statistics and probability work together.  The subject of statistics involves the study of methods for collecting, summarizing, and interpreting data.  Statistics formalizes the process of making decisions, and this course is designed to help you use statistical literacy to make better decisions.  Note that this course has applications for the natural sciences, economics, computer science, finance, psychology, sociology, criminology, and many other fields. We read data in articles and reports every day.  After finishing this course, you should be comfortable evaluating an author's use of data.  You will be able to extract information from articles and display that information effectively.  You will also be able to understand the basics of how to draw statistical conclusions. This course will begin with descriptive statistic…

Study faster and smarter for the CSET Mathematics Subtest II exam. The CSET Mathematics Subtest II covers geometry, statistics and probability.

Study faster and smarter for the CSET Mathematics Subtest III exam. The CSET Mathematics Subtest II covers geometry, statistics and probability.

Starts : 2015-09-14
No votes
Coursera Free Closed [?] Computer Sciences English Statistics and Data Analysis Teacher Professional Development

The Coursera course, Data Analysis and Statistical Inference has been revised and is now offered as part of Coursera Specialization “Statistics with R”. This course introduces you to the discipline of statistics as a science of understanding and analyzing data. You will learn how to effectively make use of data in the face of uncertainty: how to collect data, how to analyze data, and how to use data to make inferences and conclusions about real world phenomena.

Starts : 2017-09-26
No votes
edX Free Closed [?] English Data Analysis & Statistics Economics & Finance EdX MITx Social Sciences

This course is part of the MITx MicroMasters program in Data, Economics, and Development Policy (DEDP). To audit this course, click “Enroll Now” in the green button at the top of this page.

To enroll in the MicroMasters track or to learn more about this program and how it integrates with MIT’s new blended Master’s degree, go to MITx’s MicroMasters portal.

This statistics and data analysis course will introduce you to the essential notions of probability and statistics. We will cover techniques in modern data analysis: estimation, regression and econometrics, prediction, experimental design, randomized control trials (and A/B testing), machine learning, and data visualization. We will illustrate these concepts with applications drawn from real world examples and frontier research. Finally, we will provide instruction for how to use the statistical package R and opportunities for students to perform self-directed empirical analyses.

This course is designed for anyone who wants to learn how to work with data and communicate data-driven findings effectively, but it is challenging. Students who are uncomfortable with basic calculus and algebra might struggle with the pace of the class.

Starts : 2003-02-01
11 votes
MIT OpenCourseWare (OCW) Free Business Graduate MIT OpenCourseWare Sloan School of Management

Data that has relevance for managerial decisions is accumulating at an incredible rate due to a host of technological advances. Electronic data capture has become inexpensive and ubiquitous as a by-product of innovations such as the internet, e-commerce, electronic banking, point-of-sale devices, bar-code readers, and intelligent machines. Such data is often stored in data warehouses and data marts specifically intended for management decision support. Data mining is a rapidly growing field that is concerned with developing techniques to assist managers to make intelligent use of these repositories. A number of successful applications have been reported in areas such as credit rating, fraud detection, database marketing, customer relationship management, and stock market investments. The field of data mining has evolved from the disciplines of statistics and artificial intelligence.

This course will examine methods that have emerged from both fields and proven to be of value in recognizing patterns and making predictions from an applications perspective. We will survey applications and provide an opportunity for hands-on experimentation with algorithms for data mining using easy-to- use software and cases.

Starts : 2017-09-18
No votes
edX Free Closed [?] English Business & Management ColumbiaX EdX

In today’s world, managerial decisions are increasingly based on data-driven models and analysis using statistical and optimization methods that have dramatically changed the way businesses operate in most domains including service operations, marketing, transportation, and finance.

The main objectives of this course are the following:

  • Introduce fundamental techniques towards a principled approach for data-driven decision-making.
  • Quantitative modeling of dynamic nature of decision problems using historical data, and
  • Learn various approaches for decision-making in the face of uncertainty

Topics covered include probability, statistics, regression, stochastic modeling, and linear, nonlinear and discrete optimization.

Most of the topics will be presented in the context of practical business applications to illustrate its usefulness in practice. 

Starts : 2016-01-11
No votes
edX Free Closed [?] English Biology & Life Sciences Data Analysis & Statistics EdX Medicine University of TorontoX

This global health and life sciences course enables learners to investigate health problems affecting large populations – the whole world in fact! By understanding the big numbers in global mortality and their causes and distributions you will learn how to think numerically about global health. We will use real data from real people to ask the questions: What are the major causes of death in the world? Why do we need cause of death statistics? How does counting the dead help the living?
 
We begin with a historical perspective on global mortality and end with a hopeful look toward future trends. In between, you will learn about how death prior to old age can be avoided, worldwide mortality rates, and specific diseases such as HIV, malaria, childhood conditions, chronic diseases, and risk factors such as smoking. This course will help you use population statistics to understand how rapid gains in health are possible.

Starts : 2016-01-04
No votes
Coursera Free Closed [?] English Social Sciences Statistics and Data Analysis

Learn about descriptive statistics, and how they are used and misused in the social and behavioral sciences. Learn how to critically evaluate the use of descriptive statistics in published research and how to generate descriptive statistics yourself, using freely available statistical software.

No votes
Udemy $39 Closed [?] Math & Science

Learn step-by-step how to calculate a number of different descriptive statistics in SPSS.

Starts : 2009-09-01
12 votes
MIT OpenCourseWare (OCW) Free Engineering Mechanical Engineering MIT OpenCourseWare Undergraduate

This course covers the design, construction, and testing of field robotic systems, through team projects with each student responsible for a specific subsystem. Projects focus on electronics, instrumentation, and machine elements. Design for operation in uncertain conditions is a focus point, with ocean waves and marine structures as a central theme. Topics include basic statistics, linear systems, Fourier transforms, random processes, spectra, ethics in engineering practice, and extreme events with applications in design.

Starts : 2005-02-01
14 votes
MIT OpenCourseWare (OCW) Free Engineering Graduate Mechanical Engineering MIT OpenCourseWare

The course covers the basic techniques for evaluating the maximum forces and loads over the life of a marine structure or vehicle, so as to be able to design its basic configuration. Loads and motions of small and large structures and their short-term and long-term statistics are studied in detail and many applications are presented in class and studied in homework and laboratory sessions. Issues related to seakeeping of ships are studied in detail. The basic equations and issues of maneuvering are introduced at the end of the course. Three laboratory sessions demonstrate the phenomena studied and provide experience with experimental methods and data processing.

This course was originally offered in Course 13 (Ocean Engineering) as 13.42.

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